
"""simple docstring"""



import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


a       :						Union[str, Any]       		=		{
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def 			_UpperCamelCase						(    _A   )     ->	List[str]:





    """simple docstring"""

    assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
    assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
    if args.mlm:
        assert os.path.isfile(args.token_counts   )
        assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
    else:
        assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

    assert args.teacher_type == args.student_type or (
        args.student_type == "distilbert" and args.teacher_type == "bert"
    )
    assert os.path.isfile(args.student_config   )
    if args.student_pretrained_weights is not None:
        assert os.path.isfile(args.student_pretrained_weights   )

    if args.freeze_token_type_embds:
        assert args.student_type in ["roberta"]

    assert args.alpha_ce >= 0.0
    assert args.alpha_mlm >= 0.0
    assert args.alpha_clm >= 0.0
    assert args.alpha_mse >= 0.0
    assert args.alpha_cos >= 0.0
    assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Any:





    """simple docstring"""

    if args.student_type == "roberta":
        _UpperCAmelCase        = False
    elif args.student_type == "gpt2":
        _UpperCAmelCase        = False


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    if args.student_type == "roberta":
        _UpperCAmelCase        = False


def 			_UpperCamelCase						(    )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = argparse.ArgumentParser(description="""Training"""   )
    parser.add_argument("""--force"""       ,   action="""store_true"""       ,   help="""Overwrite dump_path if it already exists."""   )

    parser.add_argument(
        """--dump_path"""       ,   type=_A       ,   required=_A       ,   help="""The output directory (log, checkpoints, parameters, etc.)"""   )
    parser.add_argument(
        """--data_file"""       ,   type=_A       ,   required=_A       ,   help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""       ,   )

    parser.add_argument(
        """--student_type"""       ,   type=_A       ,   choices=["""distilbert""", """roberta""", """gpt2"""]       ,   required=_A       ,   help="""The student type (DistilBERT, RoBERTa)."""       ,   )
    parser.add_argument("""--student_config"""       ,   type=_A       ,   required=_A       ,   help="""Path to the student configuration."""   )
    parser.add_argument(
        """--student_pretrained_weights"""       ,   default=_A       ,   type=_A       ,   help="""Load student initialization checkpoint."""   )

    parser.add_argument(
        """--teacher_type"""       ,   choices=["""bert""", """roberta""", """gpt2"""]       ,   required=_A       ,   help="""Teacher type (BERT, RoBERTa)."""   )
    parser.add_argument("""--teacher_name"""       ,   type=_A       ,   required=_A       ,   help="""The teacher model."""   )

    parser.add_argument("""--temperature"""       ,   default=2.0       ,   type=_A       ,   help="""Temperature for the softmax temperature."""   )
    parser.add_argument(
        """--alpha_ce"""       ,   default=0.5       ,   type=_A       ,   help="""Linear weight for the distillation loss. Must be >=0."""   )
    parser.add_argument(
        """--alpha_mlm"""       ,   default=0.0       ,   type=_A       ,   help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""       ,   )
    parser.add_argument("""--alpha_clm"""       ,   default=0.5       ,   type=_A       ,   help="""Linear weight for the CLM loss. Must be >=0."""   )
    parser.add_argument("""--alpha_mse"""       ,   default=0.0       ,   type=_A       ,   help="""Linear weight of the MSE loss. Must be >=0."""   )
    parser.add_argument(
        """--alpha_cos"""       ,   default=0.0       ,   type=_A       ,   help="""Linear weight of the cosine embedding loss. Must be >=0."""   )

    parser.add_argument(
        """--mlm"""       ,   action="""store_true"""       ,   help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""   )
    parser.add_argument(
        """--mlm_mask_prop"""       ,   default=0.15       ,   type=_A       ,   help="""Proportion of tokens for which we need to make a prediction."""       ,   )
    parser.add_argument("""--word_mask"""       ,   default=0.8       ,   type=_A       ,   help="""Proportion of tokens to mask out."""   )
    parser.add_argument("""--word_keep"""       ,   default=0.1       ,   type=_A       ,   help="""Proportion of tokens to keep."""   )
    parser.add_argument("""--word_rand"""       ,   default=0.1       ,   type=_A       ,   help="""Proportion of tokens to randomly replace."""   )
    parser.add_argument(
        """--mlm_smoothing"""       ,   default=0.7       ,   type=_A       ,   help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""       ,   )
    parser.add_argument("""--token_counts"""       ,   type=_A       ,   help="""The token counts in the data_file for MLM."""   )

    parser.add_argument(
        """--restrict_ce_to_mask"""       ,   action="""store_true"""       ,   help="""If true, compute the distillation loss only the [MLM] prediction distribution."""       ,   )
    parser.add_argument(
        """--freeze_pos_embs"""       ,   action="""store_true"""       ,   help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""       ,   )
    parser.add_argument(
        """--freeze_token_type_embds"""       ,   action="""store_true"""       ,   help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""       ,   )

    parser.add_argument("""--n_epoch"""       ,   type=_A       ,   default=3       ,   help="""Number of pass on the whole dataset."""   )
    parser.add_argument("""--batch_size"""       ,   type=_A       ,   default=5       ,   help="""Batch size (for each process)."""   )
    parser.add_argument(
        """--group_by_size"""       ,   action="""store_false"""       ,   help="""If true, group sequences that have similar length into the same batch. Default is true."""       ,   )

    parser.add_argument(
        """--gradient_accumulation_steps"""       ,   type=_A       ,   default=5_0       ,   help="""Gradient accumulation for larger training batches."""       ,   )
    parser.add_argument("""--warmup_prop"""       ,   default=0.05       ,   type=_A       ,   help="""Linear warmup proportion."""   )
    parser.add_argument("""--weight_decay"""       ,   default=0.0       ,   type=_A       ,   help="""Weight decay if we apply some."""   )
    parser.add_argument("""--learning_rate"""       ,   default=5e-4       ,   type=_A       ,   help="""The initial learning rate for Adam."""   )
    parser.add_argument("""--adam_epsilon"""       ,   default=1e-6       ,   type=_A       ,   help="""Epsilon for Adam optimizer."""   )
    parser.add_argument("""--max_grad_norm"""       ,   default=5.0       ,   type=_A       ,   help="""Max gradient norm."""   )
    parser.add_argument("""--initializer_range"""       ,   default=0.02       ,   type=_A       ,   help="""Random initialization range."""   )

    parser.add_argument(
        """--fp16"""       ,   action="""store_true"""       ,   help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       ,   )
    parser.add_argument(
        """--fp16_opt_level"""       ,   type=_A       ,   default="""O1"""       ,   help=(
            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
            """See details at https://nvidia.github.io/apex/amp.html"""
        )       ,   )
    parser.add_argument("""--n_gpu"""       ,   type=_A       ,   default=1       ,   help="""Number of GPUs in the node."""   )
    parser.add_argument("""--local_rank"""       ,   type=_A       ,   default=-1       ,   help="""Distributed training - Local rank"""   )
    parser.add_argument("""--seed"""       ,   type=_A       ,   default=5_6       ,   help="""Random seed"""   )

    parser.add_argument("""--log_interval"""       ,   type=_A       ,   default=5_0_0       ,   help="""Tensorboard logging interval."""   )
    parser.add_argument("""--checkpoint_interval"""       ,   type=_A       ,   default=4_0_0_0       ,   help="""Checkpoint interval."""   )
    _UpperCAmelCase        = parser.parse_args()
    sanity_checks(_A   )

    # ARGS #
    init_gpu_params(_A   )
    set_seed(_A   )
    if args.is_master:
        if os.path.exists(args.dump_path   ):
            if not args.force:
                raise ValueError(
                    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                    """ itUse `--force` if you want to overwrite it"""   )
            else:
                shutil.rmtree(args.dump_path   )

        if not os.path.exists(args.dump_path   ):
            os.makedirs(args.dump_path   )
        logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""   )

        # SAVE PARAMS #
        logger.info(F"""Param: {args}"""   )
        with open(os.path.join(args.dump_path       ,   """parameters.json"""   )       ,   """w"""   ) as f:
            json.dump(vars(_A   )       ,   _A       ,   indent=4   )
        git_log(args.dump_path   )

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = MODEL_CLASSES[args.student_type]
    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = MODEL_CLASSES[args.teacher_type]

    # TOKENIZER #
    _UpperCAmelCase        = teacher_tokenizer_class.from_pretrained(args.teacher_name   )
    _UpperCAmelCase        = {}
    for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
        _UpperCAmelCase        = tokenizer.all_special_tokens.index(_A   )
        _UpperCAmelCase        = tokenizer.all_special_ids[idx]
    logger.info(F"""Special tokens {special_tok_ids}"""   )
    _UpperCAmelCase        = special_tok_ids
    _UpperCAmelCase        = tokenizer.max_model_input_sizes[args.teacher_name]

    # DATA LOADER #
    logger.info(F"""Loading data from {args.data_file}"""   )
    with open(args.data_file       ,   """rb"""   ) as fp:
        _UpperCAmelCase        = pickle.load(_A   )

    if args.mlm:
        logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""   )
        with open(args.token_counts       ,   """rb"""   ) as fp:
            _UpperCAmelCase        = pickle.load(_A   )

        _UpperCAmelCase        = np.maximum(_A       ,   1   ) ** -args.mlm_smoothing
        for idx in special_tok_ids.values():
            _UpperCAmelCase        = 0.0  # do not predict special tokens
        _UpperCAmelCase        = torch.from_numpy(_A   )
    else:
        _UpperCAmelCase        = None

    _UpperCAmelCase        = LmSeqsDataset(params=_A       ,   data=_A   )
    logger.info("""Data loader created."""   )

    # STUDENT #
    logger.info(F"""Loading student config from {args.student_config}"""   )
    _UpperCAmelCase        = student_config_class.from_pretrained(args.student_config   )
    _UpperCAmelCase        = True

    if args.student_pretrained_weights is not None:
        logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""   )
        _UpperCAmelCase        = student_model_class.from_pretrained(args.student_pretrained_weights       ,   config=_A   )
    else:
        _UpperCAmelCase        = student_model_class(_A   )

    if args.n_gpu > 0:
        student.to(F"""cuda:{args.local_rank}"""   )
    logger.info("""Student loaded."""   )

    # TEACHER #
    _UpperCAmelCase        = teacher_model_class.from_pretrained(args.teacher_name       ,   output_hidden_states=_A   )
    if args.n_gpu > 0:
        teacher.to(F"""cuda:{args.local_rank}"""   )
    logger.info(F"""Teacher loaded from {args.teacher_name}."""   )

    # FREEZING #
    if args.freeze_pos_embs:
        freeze_pos_embeddings(_A       ,   _A   )
    if args.freeze_token_type_embds:
        freeze_token_type_embeddings(_A       ,   _A   )

    # SANITY CHECKS #
    assert student.config.vocab_size == teacher.config.vocab_size
    assert student.config.hidden_size == teacher.config.hidden_size
    assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
    if args.mlm:
        assert token_probs.size(0   ) == stu_architecture_config.vocab_size

    # DISTILLER #
    torch.cuda.empty_cache()
    _UpperCAmelCase        = Distiller(
        params=_A       ,   dataset=_A       ,   token_probs=_A       ,   student=_A       ,   teacher=_A   )
    distiller.train()
    logger.info("""Let's go get some drinks."""   )


if __name__ == "__main__":
 main()

"""simple docstring"""



import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _UpperCAmelCase        = requests.get(_A       ,   headers=_A   ).json()
    _UpperCAmelCase        = {}

    try:
        job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )
        _UpperCAmelCase        = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

        for i in range(_A   ):
            _UpperCAmelCase        = requests.get(url + F"""&page={i + 2}"""       ,   headers=_A   ).json()
            job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )

        return job_links
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _UpperCAmelCase        = requests.get(_A       ,   headers=_A   ).json()
    _UpperCAmelCase        = {}

    try:
        artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )
        _UpperCAmelCase        = math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

        for i in range(_A   ):
            _UpperCAmelCase        = requests.get(url + F"""&page={i + 2}"""       ,   headers=_A   ).json()
            artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )

        return artifacts
    except Exception:
        print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = None
    if token is not None:
        _UpperCAmelCase        = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}

    _UpperCAmelCase        = requests.get(_A       ,   headers=_A       ,   allow_redirects=_A   )
    _UpperCAmelCase        = result.headers["""Location"""]
    _UpperCAmelCase        = requests.get(_A       ,   allow_redirects=_A   )
    _UpperCAmelCase        = os.path.join(_A       ,   F"""{artifact_name}.zip"""   )
    with open(_A       ,   """wb"""   ) as fp:
        fp.write(response.content   )


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = []
    _UpperCAmelCase        = []
    _UpperCAmelCase        = None

    with zipfile.ZipFile(_A   ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_A   ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_A   ) as f:
                        for line in f:
                            _UpperCAmelCase        = line.decode("""UTF-8"""   ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _UpperCAmelCase        = line[: line.index(""": """   )]
                                    _UpperCAmelCase        = line[line.index(""": """   ) + len(""": """   ) :]
                                    errors.append([error_line, error]   )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith("""FAILED """   ):
                                # `test` is the test method that failed
                                _UpperCAmelCase        = line[len("""FAILED """   ) :]
                                failed_tests.append(_A   )
                            elif filename == "job_name.txt":
                                _UpperCAmelCase        = line

    if len(_A   ) != len(_A   ):
        raise ValueError(
            F"""`errors` and `failed_tests` should have the same number of elements. Got {len(_A   )} for `errors` """
            F"""and {len(_A   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            """ problem."""   )

    _UpperCAmelCase        = None
    if job_name and job_links:
        _UpperCAmelCase        = job_links.get(_A       ,   _A   )

    # A list with elements of the form (line of error, error, failed test)
    _UpperCAmelCase        = [x + [y] + [job_link] for x, y in zip(_A       ,   _A   )]

    return result


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = []

    _UpperCAmelCase        = [os.path.join(_A       ,   _A   ) for p in os.listdir(_A   ) if p.endswith(""".zip"""   )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_A       ,   job_links=_A   )   )

    return errors


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = Counter()
    counter.update([x[1] for x in logs]   )
    _UpperCAmelCase        = counter.most_common()
    _UpperCAmelCase        = {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _UpperCAmelCase        = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

    _UpperCAmelCase        = dict(sorted(r.items()       ,   key=lambda _A   : item[1]["count"]       ,   reverse=_A   )   )
    return r


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = test.split("""::"""   )[0]
    if test.startswith("""tests/models/"""   ):
        _UpperCAmelCase        = test.split("""/"""   )[2]
    else:
        _UpperCAmelCase        = None

    return test


def 			_UpperCamelCase						(    _A       ,   _A=None   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = [(x[0], x[1], get_model(x[2]   )) for x in logs]
    _UpperCAmelCase        = [x for x in logs if x[2] is not None]
    _UpperCAmelCase        = {x[2] for x in logs}

    _UpperCAmelCase        = {}
    for test in tests:
        _UpperCAmelCase        = Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]   )
        _UpperCAmelCase        = counter.most_common()
        _UpperCAmelCase        = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _UpperCAmelCase        = sum(error_counts.values()   )
        if n_errors > 0:
            _UpperCAmelCase        = {"""count""": n_errors, """errors""": error_counts}

    _UpperCAmelCase        = dict(sorted(r.items()       ,   key=lambda _A   : item[1]["count"]       ,   reverse=_A   )   )
    return r


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = """| no. | error | status |"""
    _UpperCAmelCase        = """|-:|:-|:-|"""
    _UpperCAmelCase        = [header, sep]
    for error in reduced_by_error:
        _UpperCAmelCase        = reduced_by_error[error]["""count"""]
        _UpperCAmelCase        = F"""| {count} | {error[:1_0_0]} |  |"""
        lines.append(_A   )

    return "\n".join(_A   )


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """| model | no. of errors | major error | count |"""
    _UpperCAmelCase        = """|-:|-:|-:|-:|"""
    _UpperCAmelCase        = [header, sep]
    for model in reduced_by_model:
        _UpperCAmelCase        = reduced_by_model[model]["""count"""]
        _UpperCAmelCase     ,_UpperCAmelCase        = list(reduced_by_model[model]["""errors"""].items()   )[0]
        _UpperCAmelCase        = F"""| {model} | {count} | {error[:6_0]} | {_count} |"""
        lines.append(_A   )

    return "\n".join(_A   )


if __name__ == "__main__":
 a       :						Optional[Any]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
 parser.add_argument(
     '''--output_dir''',
     type=str,
     required=True,
     help='''Where to store the downloaded artifacts and other result files.''',
 )
 parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
 a       :						Any       		=		parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 a       :						Optional[int]       		=		get_job_links(args.workflow_run_id, token=args.token)
 a       :						Optional[int]       		=		{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    a       :						Union[str, Any]       		=		k.find(''' / ''')
    a       :						Optional[int]       		=		k[index + len(''' / ''') :]
   a       :						Optional[int]       		=		v
 with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 a       :						Union[str, Any]       		=		get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 a       :						Any       		=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 a       :						int       		=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 a       :						List[str]       		=		counter.most_common(3_0)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 a       :						List[Any]       		=		reduce_by_error(errors)
 a       :						List[str]       		=		reduce_by_model(errors)

 a       :						Dict       		=		make_github_table(reduced_by_error)
 a       :						int       		=		make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
  fp.write(sa)